Weather peril scoring is widespread in several industries. For instance, it is a vital tool for property and casualty insurance underwriters who need to know the risk of damaging weather at a location when deciding whether to issue a policy at that location and when setting appropriate premiums. Generally, the state of peril scoring is rudimentary, with scores calculated based on historical experience with large catastrophic events. Because the frequency of such events is relatively low, the scores are rarely updated, leaving users with stale scores that do not reflect the risks of weather from more minor, but still damaging events. Also, the scores are generally provided for large geographic areas, and, accordingly do not have the granularity to provide a useful prediction of the likelihood of a damaging event at a particular location. In addition, to the extent current peril scoring relies on human observational data (for example, reports of hail or tornadic storms), the scores are heavily influenced by population density. That is, there tend to be more human observations of weather events in more heavily populated areas—leading to artificially low peril scores in more rural areas. Another problem with current peril scoring paradigms is that they tend to be over-reliant on the probability of a peril's occurrence without considering the likely severity of a weather event. The resulting peril score is far less useful to end users who use the scores to determine the likelihood of a damaging event, like underwriters.